Weekdays Research Articles

Analysis of Travel Time Reliability in New York City Based on Day-of-Week and Time-of-Day Periods

Travel time reliability in New York City was analyzed with three travel time reliability measures. A classification and regression tree model was used for the analysis. Instead of analysis of conventional peak and off-peak periods, day-of-week (DOW) and time-of-day (TOD) periods were determined on the basis of each travel time reliability measure. DOW and TOD periods were identified on the basis of average travel time and each selected measure. Travel time reliability measures formulated to explain the same phenomenon classified different periods as having similar characteristics. The results agreed with the literature that reliability measures should be based on temporal periods such as DOW and TOD; however, the selection of time periods should be measure specific. The impact of New York City's urban grid network on travel time and speed distributions is also discussed. The travel time distribution patterns reported in the literature for freeways do not exist for the city. Therefore, caution is suggested for transferring reliability measures across different network structures.

Physical Activity across Body Composition and Sex in Low Income Mexican and White American Youth

PURPOSE: The purpose of this study was to determine whether physical activity (PA) differs across different body composition and sex in low income Mexican and White youth in the United States. METHODS: Participants were 195 youth (78 boys and 117 girls with a M±SD age of 14.8±1.5 years). Approximately 51% were Mexican American and 49% were White youth. PA was assessed using a piezoelectric pedometer with a moderate to vigorous physical activity (MVPA) timer. Participants wore the pedometers for 7 days and recorded daily time worn on a PA log. MANOVA was used to determine the interactions of race (White vs. Mexican), sex (M vs. F), BMI classification (<85th percentile vs. ≥85th percentile), and waist circumference (WC) classification (<90th percentile vs. ≥90th percentile) on average steps/day and average MVPA/day for the weekend (WE) and weekdays (WD). RESULTS: Results of the MANOVA indicated a significant three-way interaction between race, sex, and BMI categories for average MVPA time on the WE (p=0.034). Then a 2x2 factorial ANOVA by sex was performed using race and BMI categories as the factors. No significant interaction was found, but the males showed a significant difference between overweight and normal weight categories, with the normal weight participants performing more WE MVPA, but no difference was found between the two races. For the females, a significant main effect was found for race, with white showing higher activity than Mexican on the WE, but no difference was found between BMI categories. The MANOVA also indicated significant main effect of race for average MVPA time on the WE (p=0.002) and WD (p=0.050), showing higher MVPA in White as compared to Mexican participants. The MANOVA also showed a main effect of sex for all dependent variables (WE steps/day p<0.001, WD steps/day p=0.001, WE MVPA p=0.008, WD MVPA p=0.008), with males taking more steps/day and showing higher MVPA for both WE and WD. Chi square showed observed number of overweight youth was significantly different than expected in all groups (p<0.01). CONCLUSIONS: White were more active than Mexican American youth with Mexican American girls least active, although low PA was seen regardless of race and sex. Effective interventions to increase daily PA for low income and ethnic minority youth in the United States must become a higher public priority.

Evaluation of sliding baseline methods for spatial estimation for cluster detection in the biosurveillance system

BackgroundThe Centers for Disease Control and Prevention's (CDC's) BioSense system provides near-real time situational awareness for public health monitoring through analysis of electronic health data. Determination of anomalous spatial and temporal disease clusters is a crucial part of the daily disease monitoring task. Our study focused on finding useful anomalies at manageable alert rates according to available BioSense data history.MethodsThe study dataset included more than 3 years of daily counts of military outpatient clinic visits for respiratory and rash syndrome groupings. We applied four spatial estimation methods in implementations of space-time scan statistics cross-checked in Matlab and C. We compared the utility of these methods according to the resultant background cluster rate (a false alarm surrogate) and sensitivity to injected cluster signals. The comparison runs used a spatial resolution based on the facility zip code in the patient record and a finer resolution based on the residence zip code.ResultsSimple estimation methods that account for day-of-week (DOW) data patterns yielded a clear advantage both in background cluster rate and in signal sensitivity. A 28-day baseline gave the most robust results for this estimation; the preferred baseline is long enough to remove daily fluctuations but short enough to reflect recent disease trends and data representation. Background cluster rates were lower for the rash syndrome counts than for the respiratory counts, likely because of seasonality and the large scale of the respiratory counts.ConclusionThe spatial estimation method should be chosen according to characteristics of the selected data streams. In this dataset with strong day-of-week effects, the overall best detection performance was achieved using subregion averages over a 28-day baseline stratified by weekday or weekend/holiday behavior. Changing the estimation method for particular scenarios involving different spatial resolution or other syndromes can yield further improvement.

Overnight activities of medical students on call: is it really educational?

Background To validate self-report of activities and to assess time allocation of medical students taking in-house call during their third-year surgery clerkship. Methods Informed consent was obtained from students who agreed to participate while rotating on the third-year surgery clerkship. Students were randomized to 1 of 2 groups, either the self-report group or the shadow group. All students kept a log of their activities during weekdays. Students randomized to the self-report group kept a log of their activities on nights (6 pm to 7 am weeknights) and weekends (7 am to 7 am Saturday and Sunday) when they were assigned to in-house call. Students randomized to the shadow group kept a log of their activities and also were shadowed while on call by a research assistant (observer) who recorded their activities. All students categorized each activity as patient-care activities related to educational objectives (educational), patient-care activities unrelated to educational objectives (noneducational), and personal care (personal). To validate self-report, student and observer logs were compared. Percentages of time engaged in educational versus other activities were compared between the shadowed and nonshadowed groups and among weekdays (WD), weeknights (WN), weekend days (WED), and weekend nights (WEN). Results A total of 34 students (16 shadow, 18 self-report) agreed to participate. Five (15%) students, all in the self-report group, did not turn in their logs. Data were available for the remaining 29 students for 138 shifts (WD, 44; WN, 46; WED, 24; WEN, 24). Observer and self-report were correlated highly for educational and personal activities, but not for noneducational activities. On WD, students averaged 76.1% of their time in educational activities, compared with 46.7%, 63.3%, and 50.2% of their time while on WN, WED, and WEN, respectively ( P <.05 for WD vs. all others). Students spent between 9% and 14% of their time in noneducational activities, and between 9% and 49% of their time in personal activities during call shifts. Conclusions This study validates student self-report of activities while on surgery call. Students spend significantly more time engaged in educational activities during weekdays than any call shifts (weeknights, weekend days, weekend nights). This information can be used for curricular planning.

Monitoring and Predicting Freeway Travel Time Reliability

Generally, the day-to-day variability of route travel times on, for example, freeway corridors is considered closely related to the reliability of a road network. The more that travel times on route r are dispersed in a particular time-of-day (TOD) and day-of-week (DOW) period, the more unreliable travel times on route r are conceived to be. In the literature, many different aspects of the day-to-day travel time distribution have been proposed as indicators of reliability. Mean and variance do not provide much insight because those metrics tend to obscure important aspects of the distribution under specific circumstances. It is argued that both skew and width of this distribution are relevant indicators for unreliability; therefore, two reliability metrics are proposed. These metrics are based on three characteristic percentiles: the 10th, 50th, and 90th percentile for a given route and TOD-DOW period. High values of either metric indicate high travel time unreliability. However, the weight of each metric on travel time reliability may be application- or context-specific. The practical value of these particular metrics is that they can be used to construct so-called reliability maps, which not only visualize the unreliability of travel times for a given DOW-TOD period but also help identify DOW-TOD periods in which congestion will likely set in (or dissolve). That means identification of the uncertainty of start, end, and, hence, length of morning and afternoon peak hours. Combined with a long-term travel time prediction model, the metrics can be used to predict travel time (un)reliability. Finally, the metrics may be used in discrete choice models as explanatory variables for driver uncertainty.

Monitoring and Predicting Freeway Travel Time Reliability: Using Width and Skew of Day-to-Day Travel Time Distribution

Generally, the day-to-day variability of route travel times on, for example, freeway corridors is considered closely related to the reliability of a road network. The more that travel times on route r are dispersed in a particular time-of-day (TOD) and day-of-week (DOW) period, the more unreliable travel times on route r are conceived to be. In the literature, many different aspects of the day-to-day travel time distribution have been proposed as indicators of reliability. Mean and variance do not provide much insight because those metrics tend to obscure important aspects of the distribution under specific circumstances. It is argued that both skew and width of this distribution are relevant indicators for unreliability; therefore, two reliability metrics are proposed. These metrics are based on three characteristic percentiles: the 10th, 50th, and 90th percentile for a given route and TOD-DOW period. High values of either metric indicate high travel time unreliability. However, the weight of each metric on travel time reliability may be application- or context-specific. The practical value of these particular metrics is that they can be used to construct so-called reliability maps, which not only visualize the unreliability of travel times for a given DOW-TOD period but also help identify DOW-TOD periods in which congestion will likely set in (or dissolve). That means identification of the uncertainty of start, end, and, hence, length of morning and afternoon peak hours. Combined with a long-term travel time prediction model, the metrics can be used to predict travel time (un)reliability. Finally, the metrics may be used in discrete choice models as explanatory variables for driver uncertainty.

Using a calendar in event dating

Three experiments examined Thompson's reconstructive account for estimating the temporal location of an event. Thompson's day-of-week (DOW) model suggests that people know the DOW because diary studies have consistently produced dating errors at 7-day intervals (e.g. 0, 7, 14, 21, etc.). This study determined whether this DOW effect was produced by a calendar. It also examined whether time estimations were primarily reconstructed. A calendar improved both date and DOW estimates, but neither estimate depended on the use of a calendar. In addition, both date and DOW estimates were primarily reconstructed, but a large portion of DOW estimates were reproduced. Therefore, the argument was made that theories explaining memory for temporal location should account for both reproductive and reconstructive processes. Copyright © 2001 John Wiley & Sons, Ltd.

Using a calendar in event dating

Three experiments examined Thompson's reconstructive account for estimating the temporal location of an event. Thompson's day-of-week (DOW) model suggests that people know the DOW because diary studies have consistently produced dating errors at 7-day intervals (e.g. 0, 7, 14, 21, etc.). This study determined whether this DOW effect was produced by a calendar. It also examined whether time estimations were primarily reconstructed. A calendar improved both date and DOW estimates, but neither estimate depended on the use of a calendar. In addition, both date and DOW estimates were primarily reconstructed, but a large portion of DOW estimates were reproduced. Therefore, the argument was made that theories explaining memory for temporal location should account for both reproductive and reconstructive processes. Copyright © 2001 John Wiley & Sons, Ltd.

CRITICAL ASSESSMENT OF TRANSPORT VARIABLES ON NB &lt;1500 GM SURVIVAL

The impact of hospital of birth (Level I,II), age of transport (< 2hr, ≥2hr), severity (Apgar ≤6,≥7) & time of transport (weekdays (WD) 8a-4p, 4p-8a & weekends (WE) on survival were evaluated in transported NB <1500 Gm in 1981-82 in NYC. 324 NB transport records were reviewed from 18 Level I Hosp. (N=163) & 22 Level II Hosp. (N=161). 4 Stratified groups (GR) of varying risks were compared (see Table). Groupings were validated by logistic regression (% sruvival on risk category), r2=.995, F1,2=441.05, P<0.005. Results: (1) By X2 &/or Fischer exact test, Level II Hosp. had a significant (sig) increase in survival on WD transports in 2 high risk Gr (1&2) compared to Level I, verified by Mantel-Haenzsel testing X2=9.12,P < 0.005 with odds ratio of 8.7 in favor of Level II. This was not noted on WE with high risk Gr 1,2; (2) Lower risk Gr 3, Level I showed sig higher survival than Level II on WD. It is clear that earlier transport of high risk small neonates from Level I is essential.